§ `ƒiÇãóP—ddlmZddlmZmZddlmZGd„de¦«ZdgZdS)é)ÚPretrainedConfigé)ÚCONFIG_MAPPINGÚ AutoConfig)ÚSuperPointConfigcót‡—eZdZdZdZdeiZ ddedededede de de de de de fˆfd„ Z ˆxZ S)ÚLightGlueConfigaÇ This is the configuration class to store the configuration of a [`LightGlueForKeypointMatching`]. It is used to instantiate a LightGlue model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LightGlue [ETH-CVG/lightglue_superpoint](https://huggingface.co/ETH-CVG/lightglue_superpoint) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`): The config object or dictionary of the keypoint detector. descriptor_dim (`int`, *optional*, defaults to 256): The dimension of the descriptors. num_hidden_layers (`int`, *optional*, defaults to 9): The number of self and cross attention layers. num_attention_heads (`int`, *optional*, defaults to 4): The number of heads in the multi-head attention. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. depth_confidence (`float`, *optional*, defaults to 0.95): The confidence threshold used to perform early stopping width_confidence (`float`, *optional*, defaults to 0.99): The confidence threshold used to prune points filter_threshold (`float`, *optional*, defaults to 0.1): The confidence threshold used to filter matches initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function to be used in the hidden layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attention_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers during self-attention. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether to trust remote code when using other models than SuperPoint as keypoint detector. Examples: ```python >>> from transformers import LightGlueConfig, LightGlueForKeypointMatching >>> # Initializing a LightGlue style configuration >>> configuration = LightGlueConfig() >>> # Initializing a model from the LightGlue style configuration >>> model = LightGlueForKeypointMatching(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` Ú lightglueÚkeypoint_detector_configNéé éçffffffî?ç®Gáz®ï?çš™™™™™¹?ç{®Gáz”?ÚgeluçTFÚdescriptor_dimÚnum_hidden_layersÚnum_attention_headsÚdepth_confidenceÚwidth_confidenceÚfilter_thresholdÚinitializer_rangeÚ hidden_actÚtrust_remote_codec óp•—| |_||zdkrtd¦«‚||_||_||_|€|}||_||_||_||_| |_ t|t¦«rf|  dd¦«|d<|dtvr"tj|d|j¬¦«}nt|dd i|¤ddi¤Ž}|€tdd¬ ¦«}||_||_|d z|_| |_| |_| |_t-¦«jd i|¤ŽdS) Néz1descriptor_dim % num_heads is different from zeroÚ model_typeÚ superpointÚ _name_or_path)rÚattn_implementationÚeager)r#r©)rÚ ValueErrorrrrÚnum_key_value_headsrrrrÚ isinstanceÚdictÚgetrrÚfrom_pretrainedr Ú hidden_sizeÚintermediate_sizerÚattention_dropoutÚattention_biasÚsuperÚ__init__)Úselfr rrrr'rrrrrr.r/rÚkwargsÚ __class__s €ú‰/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/lightglue/configuration_lightglue.pyr1zLightGlueConfig.__init__Ys™ø€ð("3ˆÔà Ð/Ñ /°1Ò 4Ð 4ÝÐPÑQÔQÐ Qà,ˆÔØ!2ˆÔØ#6ˆÔ ð Ð &Ø"5Ð à#6ˆÔ à 0ˆÔØ 0ˆÔØ 0ˆÔØ!2ˆÔõ Ð.µÑ 5Ô 5ð Ø5M×5QÒ5QÐR^Ð`lÑ5mÔ5mÐ $ \Ñ 2Ø'¨ Ô5½^ÐKÐKÝ+5Ô+EØ,¨_Ô=ÐQUÔQgð,ñ,ô,Ð(Ð(õ,:Ð:RÐS_Ô:`Ô+að,ð,Ø.ð,ð,ØDKð,ð,ð,Ð(ð $Ð +Ý'5°lÔ'CÐX_Ð'`Ñ'`Ô'`Ð $à(@ˆÔ%à)ˆÔØ!/°!Ñ!3ˆÔØ$ˆŒØ!2ˆÔØ,ˆÔ؉ŒÔÐ"Ð"˜6Ð"Ð"Ð"Ð"Ð"ó) Nr r rNrrrrrrTF)Ú__name__Ú __module__Ú __qualname__Ú__doc__r rÚ sub_configsrÚintÚfloatÚstrÚboolr1Ú __classcell__)r4s@r5r r séø€€€€€ð8ð8ðt€JØ-¨zÐ:€Kð6:Ø!Ø!"Ø#$Ø Ø"&Ø"&Ø"%Ø#'Ø ØØØ"'ð?#ð?#à"2ð?#ðð?#ðð ?#ð !ð ?#ð ð?#ð ð?#ð ð?#ð!ð?#ðð?#ð ð?#ð?#ð?#ð?#ð?#ð?#ð?#ð?#ð?#ð?#r6r N) Úconfiguration_utilsrÚautorrr!rr Ú__all__r%r6r5úrDs…ðð,4Ð3Ð3Ð3Ð3Ð3Ø-Ð-Ð-Ð-Ð-Ð-Ð-Ð-Ø)Ð)Ð)Ð)Ð)Ð)ð}#ð}#ð}#ð}#ð}#Ð&ñ}#ô}#ð}#ð@ Ð €€€r6